Added predictive value of high-throughput molecular data to clinical data and its validation

Area of Science:

• Bioinformatics
• Clinical Bioinformatics
• Biostatistics

Background:

• Numerous molecular signatures are proposed for patient outcome prediction using high-dimensional data.
• Many proposed molecular signatures fail validation, highlighting the need for robust assessment methods.
• Clinical predictors are often available, yet their integration with molecular data for enhanced prediction is underexplored.

Purpose of the Study:

• To review and critically survey procedures for assessing and validating the added predictive value of high-dimensional molecular data.
• To address the statistical and bioinformatics gap in evaluating molecular signatures when clinical predictors are already established.
• To explore methods for constructing combined prediction models using both clinical and molecular data.

Main Methods:

• Review of existing procedures for assessing added predictive value.
• Critical survey of approaches for constructing combined prediction models.
• Discussion of validation strategies using independent datasets and single-dataset assessments.

Main Results:

• Identified a gap in the literature regarding the evaluation of added predictive value of molecular signatures alongside clinical predictors.
• Surveyed various methods for combining clinical and molecular data in prediction models.
• Highlighted the importance of independent validation for molecular research findings.

Conclusions:

• Robust validation of molecular signatures is essential for reliable clinical bioinformatics.
• Methods for assessing the added predictive value of molecular data in the presence of clinical predictors are critical.
• Further development and application of these validation procedures are needed to improve patient outcome prediction.